"""simple docstring"""



import pickle

import numpy as np
from matplotlib import pyplot as plt






class  snake_case__      :


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase=0.2    ,		lowerCamelCase=0.2						):
        __a       =    bp_numa
        __a       =    bp_numa
        __a       =    bp_numa
        __a       =    conva_get[:2]
        __a       =    conva_get[2]
        __a       =    size_pa
        __a       =    rate_w
        __a       =    rate_t
        __a       =    [
            np.mat(-1 * np.random.rand(self.conva[0]    ,		self.conva[0]						) + 0.5						)
            for i in range(self.conva[1]						)
        ]
        __a       =    np.mat(-1 * np.random.rand(self.num_bpa    ,		self.num_bpa						) + 0.5						)
        __a       =    np.mat(-1 * np.random.rand(self.num_bpa    ,		self.num_bpa						) + 0.5						)
        __a       =    -2 * np.random.rand(self.conva[1]						) + 1
        __a       =    -2 * np.random.rand(self.num_bpa						) + 1
        __a       =    -2 * np.random.rand(self.num_bpa						) + 1


 def 						a__   ( self    ,		lowerCamelCase						):
        # save model dict with pickle
        __a       =    {
            "num_bp1": self.num_bpa,
            "num_bp2": self.num_bpa,
            "num_bp3": self.num_bpa,
            "conv1": self.conva,
            "step_conv1": self.step_conva,
            "size_pooling1": self.size_poolinga,
            "rate_weight": self.rate_weight,
            "rate_thre": self.rate_thre,
            "w_conv1": self.w_conva,
            "wkj": self.wkj,
            "vji": self.vji,
            "thre_conv1": self.thre_conva,
            "thre_bp2": self.thre_bpa,
            "thre_bp3": self.thre_bpa,
        }
        with open(lowerCamelCase    ,		"wb"						) as f:
               pickle.dump(lowerCamelCase    ,		lowerCamelCase						)

        print(F"Model saved： {save_path}"						)


 @classmethod
 def 						a__   ( cls    ,		lowerCamelCase						):
        # read saved model
        with open(lowerCamelCase    ,		"rb"						) as f:
               __a       =    pickle.load(lowerCamelCase						)  # noqa: S301

        __a       =    model_dic.get("conv1"						)
        conv_get.append(model_dic.get("step_conv1"						)						)
        __a       =    model_dic.get("size_pooling1"						)
        __a       =    model_dic.get("num_bp1"						)
        __a       =    model_dic.get("num_bp2"						)
        __a       =    model_dic.get("num_bp3"						)
        __a       =    model_dic.get("rate_weight"						)
        __a       =    model_dic.get("rate_thre"						)
        # create model instance
        __a       =    CNN(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)
        # modify model parameter
        __a       =    model_dic.get("w_conv1"						)
        __a       =    model_dic.get("wkj"						)
        __a       =    model_dic.get("vji"						)
        __a       =    model_dic.get("thre_conv1"						)
        __a       =    model_dic.get("thre_bp2"						)
        __a       =    model_dic.get("thre_bp3"						)
        return conv_ins


 def 						a__   ( self    ,		lowerCamelCase						):
        return 1 / (1 + np.exp(-1 * x						))


 def 						a__   ( self    ,		lowerCamelCase						):
        return round(lowerCamelCase    ,		3						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        # convolution process
        __a       =    convs[0]
        __a       =    convs[1]
        __a       =    np.shape(lowerCamelCase						)[0]
        # get the data slice of original image data, data_focus
        __a       =    []
        for i_focus in range(0    ,		size_data - size_conv + 1    ,		lowerCamelCase						):
               for j_focus in range(0    ,		size_data - size_conv + 1    ,		lowerCamelCase						):
                      __a       =    data[
                          i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
                      ]
                      data_focus.append(lowerCamelCase						)
        # calculate the feature map of every single kernel, and saved as list of matrix
        __a       =    []
        __a       =    int((size_data - size_conv) / conv_step + 1						)
        for i_map in range(lowerCamelCase						):
               __a       =    []
               for i_focus in range(len(lowerCamelCase						)						):
                      __a       =    (
                          np.sum(np.multiply(data_focus[i_focus]    ,		w_convs[i_map]						)						)
                          - thre_convs[i_map]
                      )
                      featuremap.append(self.sig(lowerCamelCase						)						)
               __a       =    np.asmatrix(lowerCamelCase						).reshape(
                   lowerCamelCase    ,		lowerCamelCase						)
               data_featuremap.append(lowerCamelCase						)

        # expanding the data slice to One dimenssion
        __a       =    []
        for each_focus in data_focus:
               focusa_list.extend(self.Expand_Mat(lowerCamelCase						)						)
        __a       =    np.asarray(lowerCamelCase						)
        return focus_list, data_featuremap


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase="average_pool"						):
        # pooling process
        __a       =    len(featuremaps[0]						)
        __a       =    int(size_map / size_pooling						)
        __a       =    []
        for i_map in range(len(lowerCamelCase						)						):
               __a       =    featuremaps[i_map]
               __a       =    []
               for i_focus in range(0    ,		lowerCamelCase    ,		lowerCamelCase						):
                      for j_focus in range(0    ,		lowerCamelCase    ,		lowerCamelCase						):
                             __a       =    feature_map[
                                 i_focus : i_focus + size_pooling,
                                 j_focus : j_focus + size_pooling,
                             ]
                             if pooling_type == "average_pool":
                                    # average pooling
                                    map_pooled.append(np.average(lowerCamelCase						)						)
                             elif pooling_type == "max_pooling":
                                    # max pooling
                                    map_pooled.append(np.max(lowerCamelCase						)						)
               __a       =    np.asmatrix(lowerCamelCase						).reshape(lowerCamelCase    ,		lowerCamelCase						)
               featuremap_pooled.append(lowerCamelCase						)
        return featuremap_pooled


 def 						a__   ( self    ,		lowerCamelCase						):
        # expanding three dimension data to one dimension list
        __a       =    []
        for i in range(len(lowerCamelCase						)						):
               __a       =    np.shape(data[i]						)
               __a       =    data[i].reshape(1    ,		shapes[0] * shapes[1]						)
               __a       =    data_listed.getA().tolist()[0]
               data_expanded.extend(lowerCamelCase						)
        __a       =    np.asarray(lowerCamelCase						)
        return data_expanded


 def 						a__   ( self    ,		lowerCamelCase						):
        # expanding matrix to one dimension list
        __a       =    np.asarray(lowerCamelCase						)
        __a       =    np.shape(lowerCamelCase						)
        __a       =    data_mat.reshape(1    ,		shapes[0] * shapes[1]						)
        return data_expanded


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    []
        __a       =    0
        for i_map in range(lowerCamelCase						):
               __a       =    np.ones((size_map, size_map)						)
               for i in range(0    ,		lowerCamelCase    ,		lowerCamelCase						):
                      for j in range(0    ,		lowerCamelCase    ,		lowerCamelCase						):
                             __a       =    pd_pool[
                                 i_pool
                             ]
                             __a       =    i_pool + 1
               __a       =    np.multiply(
                   lowerCamelCase    ,		np.multiply(out_map[i_map]    ,		(1 - out_map[i_map])						)						)
               pd_all.append(lowerCamelCase						)
        return pd_all


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase=bool						):
        # model traning
        print("----------------------Start Training-------------------------"						)
        print((" - - Shape: Train_Data  ", np.shape(lowerCamelCase						))						)
        print((" - - Shape: Teach_Data  ", np.shape(lowerCamelCase						))						)
        __a       =    0
        __a       =    []
        __a       =    10000
        while rp < n_repeat and mse >= error_accuracy:
               __a       =    0
               print(F"-------------Learning Time {rp}--------------"						)
               for p in range(len(lowerCamelCase						)						):
                      # print('------------Learning Image: %d--------------'%p)
                      __a       =    np.asmatrix(datas_train[p]						)
                      __a       =    np.asarray(datas_teach[p]						)
                      __a ,   __a       =    self.convolute(
                          lowerCamelCase    ,		self.conva    ,		self.w_conva    ,		self.thre_conva    ,		conv_step=self.step_conva    ,		)
                      __a       =    self.pooling(lowerCamelCase    ,		self.size_poolinga						)
                      __a       =    np.shape(lowerCamelCase						)
                      __a       =    self._expand(lowerCamelCase						)
                      __a       =    data_bp_input

                      __a       =    np.dot(lowerCamelCase    ,		self.vji.T						) - self.thre_bpa
                      __a       =    self.sig(lowerCamelCase						)
                      __a       =    np.dot(lowerCamelCase    ,		self.wkj.T						) - self.thre_bpa
                      __a       =    self.sig(lowerCamelCase						)

                      # --------------Model Leaning ------------------------
                      # calculate error and gradient---------------
                      __a       =    np.multiply(
                          (data_teach - bp_outa)    ,		np.multiply(lowerCamelCase    ,		(1 - bp_outa)						)						)
                      __a       =    np.multiply(
                          np.dot(lowerCamelCase    ,		self.wkj						)    ,		np.multiply(lowerCamelCase    ,		(1 - bp_outa)						)						)
                      __a       =    np.dot(lowerCamelCase    ,		self.vji						)

                      __a       =    pd_i_all / (self.size_poolinga * self.size_poolinga)
                      __a       =    pd_conva_pooled.T.getA().tolist()
                      __a       =    self._calculate_gradient_from_pool(
                          lowerCamelCase    ,		lowerCamelCase    ,		shape_featuremapa[0]    ,		shape_featuremapa[1]    ,		self.size_poolinga    ,		)
                      # weight and threshold learning process---------
                      # convolution layer
                      for k_conv in range(self.conva[1]						):
                             __a       =    self._expand_mat(pd_conva_all[k_conv]						)
                             __a       =    self.rate_weight * np.dot(lowerCamelCase    ,		lowerCamelCase						)

                             __a       =    self.w_conva[k_conv] + delta_w.reshape(
                                 (self.conva[0], self.conva[0])						)

                             __a       =    (
                                 self.thre_conva[k_conv]
                                 - np.sum(pd_conva_all[k_conv]						) * self.rate_thre
                             )
                      # all connected layer
                      __a       =    self.wkj + pd_k_all.T * bp_outa * self.rate_weight
                      __a       =    self.vji + pd_j_all.T * bp_outa * self.rate_weight
                      __a       =    self.thre_bpa - pd_k_all * self.rate_thre
                      __a       =    self.thre_bpa - pd_j_all * self.rate_thre
                      # calculate the sum error of all single image
                      __a       =    np.sum(abs(data_teach - bp_outa						)						)
                      error_count += errors
                      # print('   ----Teach      ',data_teach)
                      # print('   ----BP_output  ',bp_out3)
               __a       =    rp + 1
               __a       =    error_count / patterns
               all_mse.append(lowerCamelCase						)

        def draw_error():
               __a       =    [error_accuracy for i in range(int(n_repeat * 1.2						)						)]
               plt.plot(lowerCamelCase    ,		"+-"						)
               plt.plot(lowerCamelCase    ,		"r--"						)
               plt.xlabel("Learning Times"						)
               plt.ylabel("All_mse"						)
               plt.grid(lowerCamelCase    ,		alpha=0.5						)
               plt.show()

        print("------------------Training Complished---------------------"						)
        print((" - - Training epoch: ", rp, F"     - - Mse: {mse:.6f}")						)
        if draw_e:
               draw_error()
        return mse


 def 						a__   ( self    ,		lowerCamelCase						):
        # model predict
        __a       =    []
        print("-------------------Start Testing-------------------------"						)
        print((" - - Shape: Test_Data  ", np.shape(lowerCamelCase						))						)
        for p in range(len(lowerCamelCase						)						):
               __a       =    np.asmatrix(datas_test[p]						)
               __a ,   __a       =    self.convolute(
                   lowerCamelCase    ,		self.conva    ,		self.w_conva    ,		self.thre_conva    ,		conv_step=self.step_conva    ,		)
               __a       =    self.pooling(lowerCamelCase    ,		self.size_poolinga						)
               __a       =    self._expand(lowerCamelCase						)

               __a       =    data_bp_input
               __a       =    bp_outa * self.vji.T - self.thre_bpa
               __a       =    self.sig(lowerCamelCase						)
               __a       =    bp_outa * self.wkj.T - self.thre_bpa
               __a       =    self.sig(lowerCamelCase						)
               produce_out.extend(bp_outa.getA().tolist()						)
        __a       =    [list(map(self.do_round    ,		lowerCamelCase						)						) for each in produce_out]
        return np.asarray(lowerCamelCase						)






 def 						a__   ( self    ,		lowerCamelCase						):
        # return the data of image after convoluting process so we can check it out
        __a       =    np.asmatrix(lowerCamelCase						)
        __a ,   __a       =    self.convolute(
            lowerCamelCase    ,		self.conva    ,		self.w_conva    ,		self.thre_conva    ,		conv_step=self.step_conva    ,		)
        __a       =    self.pooling(lowerCamelCase    ,		self.size_poolinga						)

        return data_conveda, data_pooleda


if __name__ == "__main__":
   pass

"""simple docstring"""



import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE__:List[str]									=			"""us-east-1"""  # defaults region






@dataclass
class  snake_case__      :
 _snake_case       :					str
 _snake_case       :					Optional[Any]							  =						"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
 _snake_case       :					Optional[Any]							  =						{
     """task_name""": """mnli""",
     """per_device_train_batch_size""": 16,
     """per_device_eval_batch_size""": 16,
     """do_train""": True,
     """do_eval""": True,
     """do_predict""": True,
     """output_dir""": """/opt/ml/model""",
     """overwrite_output_dir""": True,
     """max_steps""": 500,
     """save_steps""": 5_500,
 }
 _snake_case       :					List[str]							  =						{**hyperparameters, """max_steps""": 1_000}


 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                   {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
               ]
        else:
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                   {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
               ]


 @property
 def 						a__   ( self						):
        return F"{self.framework}-transfromers-test"


 @property
 def 						a__   ( self						):
        return F"./tests/sagemaker/scripts/{self.framework}"






 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
        else:
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"



@pytest.fixture(scope="class"				)
def 	_lowerCamelCase(	a				):
       __a       =    SageMakerTestEnvironment(framework=request.cls.framework				)
